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https://www.artificialconversation.com/p/breaking-metas-conversational-chatbot
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Internet of Things (IoT) has enabled customers in multiple industries, such as manufacturing, automotive, and energy, to monitor and control real-world environments. By deploying a variety of edge IoT devices such as cameras, thermostats, and sensors, you can collect data, send it to the cloud, and build machine learning (ML) models to predict anomalies, failures, […]
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Saw this datascience interview question posted.
Let’s say you work at Google. You are developing a spam classifier to classify emails into spam vs. non-spam categories based on their content.
You try several different classifiers like SVM, Random Forests, etc., but none of them produce satisfactory results. So, you decide to combine them together by using stacking.
What classifier should you use as the meta-classifier in your stacking model and why?
So from my understanding, a meta classifier is essentially a model that takes as input feature the output from other models, and then provides a final prediction based on that. But what arguments are there for using a specific classifier as the top classifier?
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Machine learning is being used in almost every industry, including healthcare. However, due to the intrinsic complexity of healthcare data, classical machine learning faces various difficulties while dealing with these data. This is because healthcare outcomes like mortality, stroke, cancer initiation, and readmission frequently have a continuous time to events. Since time-to-event data frequently contains individuals whose outcomes are missing or censored owing to loss of follow-up, dealing with this type of data is much more difficult. The researchers have established that traditional classification and regression methods do not offer a simple solution to dealing with such clinical data.
Many researchers have been interested in applying deep neural networks, which may be used to create nonlinear representations of complex clin
A new study by Auton Lab at Carnegie Mellon University introduced the auton-survival package, a comprehensive Python library of user-friendly tools for machine learning applications in the presence of censored time-to-event data.
Continue reading | Check out the paper, package
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https://blenderbot.ai/
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https://www.kaggle.com/competitions/digital-turbine-auction-bid-price-prediction/overview
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Here is the link to the repo https://github.com/Shreyz-max/Doodle-to-Image-Generator
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I'm using this https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Add_image_embeddings_to_LayoutLM.ipynb#scrollTo=i_IR1xhWMwty to prepares the data but it lacks of entities as output.
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Here's a link to the post.
As an aside, I'm Varun, CEO at Exafunction. We help companies do deep learning efficiently at scale. We're excited to start sharing the best practices for using GPUs that we've learned working at cutting edge deep learning companies in the past and with our current customers. Would love if you had any suggestions for deep dives we could do.
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We did an episode with Hod Lipson & Boyuan Chen on self-modeling of robot morphologies and sentient machines. I hope you may find it useful
Podcast Link:
Video: https://youtu.be/vR-5w7i2on8
Audio: https://soundcloud.com/ieeeras-softrobotics/hod-lipson-boyuan-chen-self-modeling-of-robot-morphologies?utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing
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Here is the link to the repo https://github.com/Shreyz-max/Doodle-to-Image-Generator
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There are a few episodes of Attack on Titan that never came out in English. I've gotten about 30 minutes of isolated dialogue from each character and am praying someone knows of a TTS I can use.
tortoise-tts won't work because it uses it's own voices as a base so everyone comes out with a british accent.
And other entities with their TTS such as google or describe likely aren't going to let me use the copyrighted material.
If anyone knows something that can pull this off you'd be a godsend.
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One of the most fascinating things about virtual reality (VR) is the way it has evolved over time. You may have tried some basic 3D…
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Three techniques to process text: TFIDF, word2vector trained on our data, Gensim w2v — Natural language processing (NLP)
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3D content creators are clamoring for NVIDIA Instant NeRF, an inverse rendering tool that turns a set of static images into a realistic 3D scene. Since its debut earlier this year, tens of thousands of developers around the world have downloaded the source code and used it to render spectacular scenes, sharing eye-catching results on Read article >
The post NVIDIA Instant NeRF Wins Best Paper at SIGGRAPH, Inspires Creative Wave Amid Tens of Thousands of Downloads appeared first on NVIDIA Blog.
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Hello,
I would like to watch the talks/videos for accepted ICML 2022 papers. In the past, these used to be available for free at https://slideslive.com/library.
For example, the oral presentations (https://icml.cc/virtual/2022/events/oral) cannot be accessed without registration. However, with the conference being over, registrations are closed already.
Any ideas and tips on how to watch the videos would be very appreciated.
Thanks!
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Found a nice experiment on using sensor fusion and machine learning to detect smoke and get notified if the fire starts. Check this out: https://www.hackster.io/stefanblattmann/real-time-smoke-detection-with-ai-based-sensor-fusion-1086e6
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Innovative technologies in AI, virtual worlds and digital humans are shaping the future of design and content creation across every industry. Experience the latest advances from NVIDIA in all these areas at SIGGRAPH, the world’s largest gathering of computer graphics experts, running Aug. 8-11. At the conference, creators, developers, engineers, researchers and students will see Read article >
The post Dive Into AI, Avatars and the Metaverse With NVIDIA at SIGGRAPH appeared first on NVIDIA Blog.
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Pinterest has engineered a way to serve its photo-sharing community more of the images they love. The social-image service, with more than 400 million monthly active users, has trained bigger recommender models for improved accuracy at predicting people’s interests. Pinterest handles hundreds of millions of user requests an hour on any given day. And it Read article >
The post Pinterest Boosts Home Feed Engagement 16% With Switch to GPU Acceleration of Recommenders appeared first on NVIDIA Blog.
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It’s the first GFN Thursday of the month and you know the drill — GeForce NOW is bringing a big batch of games to the cloud. Get ready for 38 exciting titles like Saints Row and Rumbleverse arriving on the GeForce NOW library in August. Members can kick off the month streaming 13 new games Read article >
The post Rush Into August This GFN Thursday With 38 New Games on GeForce NOW appeared first on NVIDIA Blog.
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This is a guest post by Viktor Enrico Jeney, Senior Machine Learning Engineer at Adspert. Adspert is a Berlin-based ISV that developed a bid management tool designed to automatically optimize performance marketing and advertising campaigns. The company’s core principle is to automate maximization of profit of ecommerce advertising with the help of artificial intelligence. The […]
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submitted by /u/oodmb
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AI Weirdness: the strange side of machine learning
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If you can build a Machine Learning model — you should be able to deploy it
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https://reddit.com/link/wfl4nc/video/5ntmbzj9zkf91/player
https://reddit.com/link/wfl4nc/video/vul525t9zkf91/player
https://reddit.com/link/wfl4nc/video/13b738nbzkf91/player
Programming
Function from Description
Code to Explanation
Fix invalid Code
Translate Languages
Class from Description
Get Language from Code
Function from Docstring
Helpers
Regex from Description
Regex to Explanation
Linux Command
Get time complexity
Git Command from Description
Database
Text Description to SQL Command
Web
Generate HTML from Description
CSS from Description
Meta Tags from Description
I think this could be helpful to a lot of people (especially for beginner programmers). You can check out all functionalities on your own here:
programming-helper.com
Have fun using the tool ❤️
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Wondering about your workflow to train large models or run batch jobs that are either too big for you laptop? Do you use AWS VMs to run them and shut them back down after, SageMaker or AzureML?
I'm asking because I recently started working with https://github.com/dstackai/dstack which lets you run python jobs in AWS from your CLI but I'm not sure how others run their ML jobs.
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What are the Red Flags for Neural Suffering?
By [redacted] and [redacted]
Abstract:
Which kind of evidence would we need to see to believe that artificial neural networks can suffer? We review neuroscience literature, investigate behavioral arguments and propose high-level considerations that could shift our beliefs. Of these three approaches, we believe that high-level considerations, i.e. understanding under which circumstances suffering arises as an optimal training strategy, is the most promising. Our main finding, however, is that the understanding of artificial suffering is very limited and should likely get more attention.
- -
Seeds of Science is a new journal (funded through Scott Alexander's ACX grants program) that publishes speculative or non-traditional articles on scien…
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Seeds of Science is a new journal (funded through Scott Alexander's ACX grants program) that publishes speculative or non-traditional articles on scientific topics. Peer review is conducted through community-based voting and commenting by a diverse network of reviewers (or "gardeners" as we call them).
We just sent out an article for review - "What are the Red Flags for Neural Network Suffering?" - that may be of interest to some in the r/MachineLearning, so I wanted to see if anyone would be interested in joining us a gardener to review the article. It is free to join and anyone is welcome (we currently have gardeners from all levels of academia and outside of it). Participation is entirely voluntary - we send you submitted articles and you can choose to vote/comment or abstain without …
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TL;DR: TorchDynamo (prototype from PyTorch team) plus nvfuser (from Nvidia) backend makes Bert (the tool is model agnostic) inference on PyTorch > 3X faster most of the time (it depends on input shape) by just adding a single line of code in Python script. The surprising thing is that during the benchmark, we have not seen any drawback implied by the use of this library, the acceleration just comes for free. On the same model, TensorRT is (of course) much faster, > 5X at least (and even more at batch size 1 which is impressive) but comes with its own complexity. The tool being a prototype, better performances are to be expected with more mature support of some backends, in particular regarding fx2trt (aka TensorRT mixed with PyTorch)!
Our TorchDynamo benchmark notebook can be found there:…
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I do not know if this is the correct subreddit to post this or not (if not please guide me) but I need a better voice annotation tool than this one (https://github.com/gong-io/gecko). Can anyone help?
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Amazon Comprehend is a natural-language processing (NLP) service you can use to automatically extract entities, key phrases, language, sentiments, and other insights from documents. For example, you can immediately start detecting entities such as people, places, commercial items, dates, and quantities via the Amazon Comprehend console, AWS Command Line Interface, or Amazon Comprehend APIs. In […]
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Amazon SageMaker Feature Store helps data scientists and machine learning (ML) engineers securely store, discover, and share curated data used in training and prediction workflows. Feature Store is a centralized store for features and associated metadata, allowing features to be easily discovered and reused by data scientist teams working on different projects or ML models. […]
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Posted by Qifei Wang, Senior Software Engineer, and Feng Yang, Senior Staff Software Engineer, Google Research
Deep learning models for visual tasks (e.g., image classification) are usually trained end-to-end with data from a single visual domain (e.g., natural images or computer generated images). Typically, an application that completes visual tasks for multiple domains would need to build multiple models for each individual domain, train them independently (meaning no data is shared between domains), and then at inference time each model would process domain-specific input data. However, early layers between these models generate similar features, even for different domains, so it can be more efficient — decreasing latency and power consumption, lower memory overhead to store parameter…
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If you have 8 minutes to spare for my research project, follow the link below!
I'd like to hear your hypotheses about what leads people to see AI risk as important. I will test the most promising ones in a future poll. Many thanks!
https://docs.google.com/forms/d/e/1FAIpQLScT7M4_FssgBm6vvypNBW4gagzvESu5kJGP1j21CaU3N88rVw/viewform?usp=sf_link
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2 years ago, someone released an audio deepfake of Jordan Peterson reading absurdly vulgar rap lyrics. It was pretty amazing: video here
I want to learn how this was done and if any improvements to this process have been implemented since. What’s the easiest and most straightforward way to feed an algorithm hours of audio content of a person’s voice and synthesize an artificial replica of their voice that you can make say anything?
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https://morioh.com/p/afca6f2eec16
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Bringing new AI and robotics applications and products to market, or supporting existing ones, can be challenging for developers and enterprises. The NVIDIA Jetson AGX Orin 32GB production module — available now — is here to help. Nearly three dozen technology providers in the NVIDIA Partner Network worldwide are offering commercially available products powered by Read article >
The post NVIDIA Jetson AGX Orin 32GB Production Modules Now Available; Partner Ecosystem Appliances and Servers Arrive appeared first on NVIDIA Blog.
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Autonomous vehicles are one of the most complex AI challenges of our time. For AVs to operate safely in the real world, the networks running within them must come together as an intricate symphony, which requires intensive training, testing and validation on massive amounts of data. Clément Farabet, vice president of AI infrastructure at NVIDIA, Read article >
The post Music to the Gears: NVIDIA’s Clément Farabet on Orchestrating AI Training for Autonomous Vehicles appeared first on NVIDIA Blog.
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Researchers use machine learning to automatically solve, explain, and generate university-level math problems at a human level.
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Meta AI released ViTDet - transformer based model for low shot object detection. It outperforms previous models on Large Vocabulary Instance Segmentation (LVIS) dataset.
Arxiv
Blog post
They have released code in their Detectron2 library.
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so I ran the jupyter notebook of Cascade TabNet Demo.ipynb and receive what I expected, but now I'm interested in receiving the exact position of the boxes, for example: (340, 400, 762, 700), something like that..
I need this to crop this area and put it on an separated image.
notebook: https://colab.research.google.com/drive/1lzjbBQsF4X2C2WZhxBJz0wFEQor7F-fv?usp=sharing#scrollTo=e0P85mJJQ304
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Hello,
I am trying to build a disease prediction system using this dataset: https://www.kaggle.com/datasets/kaushil268/disease-prediction-using-machine-learning
What are the things I should keep in mind when cleaning the data? Does this kind of data also requires patient's demographic data, weight, height, etc. along with the symptoms of diseases?
What algorithms should I apply to train my network?
I already have training.csv and testing.csv so I don't have to split my data into 80/20, right?
Also, pour in some suggestions that you would recommend when designing such system. This is for a university thesis.
Thanks
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Hi ML folks, I've worked on ML in industry for quite some time, for example, at Google and PathAI (a startup in the healthcare space). But I've found that the research narrative around "AI" seems to be—to put it nicely—not aligned with its predominant economic uses. Some of this was discussed quite nicely in the book, The Myth of Artificial Intelligence, by Erik J. Larson. But I felt that he lacked an answer to: why are we building "AI" at all? Or what exactly are we building now?
So I investigated on my own and wrote my thoughts here. They're phrased as a response to Rich Sutton's essay, The Bitter Lesson, from a few years ago, which I find to be completely disconnected to how AI/ML is actually being used in industry.
Anyways, I am curious what this community's thoughts are on the matter...
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Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn any intellectual task that a human being can. Recently, AGI has been in the news with the Lambda sentient discussion We tend to think of AGI as a technical (algorithmic / data-driven) concept But the driver for AGI in our lives… Read More »The catalyst for AGI in our lives could be cultural rather than technical
The post The catalyst for AGI in our lives could be cultural rather than technical appeared first on Data Science Central.
( 17
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Banks and financial organizations continue to face myriad challenges in the market, such as data privacy concerns, accessibility to crucial banking data, and demand for better customer services, among many others. And it is increasingly recognized that the cloud is more than a technology; it enables banks and other financial services firms to store data… Read More »Banking and Financial Sector: Key Benefits of the Multi-Cloud Approach
The post Banking and Financial Sector: Key Benefits of the Multi-Cloud Approach appeared first on Data Science Central.
( 18
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The urban population across the world is increasing rapidly, leading to several challenges such as sanitation, traffic congestion, environmental imbalance, pollution, and others. Rapid urbanization has led to the migration of the rural population to urban areas, which has made the daily routine of the urban population convenient and comfortable. Thus, the need to incorporate… Read More »IoT Proves an Essential Component In Managing Traffic in Smart Cities
The post IoT Proves an Essential Component In Managing Traffic in Smart Cities appeared first on Data Science Central.
( 19
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Due to the rapid pace of technological change, the way we trade the stock market is becoming more complex. One of the most significant changes that have occurred is the emergence of algorithmic trading, which has allowed traders to improve their skills and compete against other individuals. This type of trading has also raised the… Read More »Replacing Traders With Algorithms: Success Stories of Real Funds
The post Replacing Traders With Algorithms: Success Stories of Real Funds appeared first on Data Science Central.
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After data scientists carefully come up with a satisfying machine learning (ML) model, the model must be deployed to be easily accessible for inference by other members of the organization. However, deploying models at scale with optimized cost and compute efficiencies can be a daunting and cumbersome task. Amazon SageMaker endpoints provide an easily scalable […]
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min )
New research ties inaccuracies in pulse oximeter readings to racial disparities in treatment and outcomes.
( 6
min )
Researchers train a machine-learning model to monitor and adjust the 3D printing process to correct errors in real-time.
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min )
3D phenom FESQ joins us 'In the NVIDIA Studio' this week to share his sensational and surreal animation 'Double/Sided' as well as an inside look into his creative workflow. 'Double/Sided' is deeply personal to FESQ, who said the piece “translates really well to a certain period of my life when I was juggling both a programmer career and an artist career.”
The post Sensational Surrealism Astonishes This Week ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
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min )
In the main 2020 procgen competition (https://www.aicrowd.com/challenges/neurips-2020-procgen-competition), OpenAI listed there as being 4 additional "private test environments". Have these ever been publicly released, and if so could someone please link me to them?
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Discover how Artificial Intelligence helps retailers profit from AI implementation. We’ve collected successful AI solutions in the retail business and real-life examples: https://exadel.com/news/how-is-ai-used-in-retail-business
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Feature engineering is one of the most challenging aspects of the machine learning (ML) lifecycle and a phase where the most amount of time is spent—data scientists and ML engineers spend 60–70% of their time on feature engineering. AWS introduced Amazon SageMaker Feature Store during AWS re:Invent 2020, which is a purpose-built, fully managed, centralized […]
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While still in grad school, Antonio Serrano-Muñoz has helped author papers spanning planetary gravities, AI-powered diagnosis of rheumatoid arthritis and robots that precisely track millimetric-sized walkers, like ants.
The post Meet the Omnivore: Developer Builds Bots With NVIDIA Omniverse and Isaac Sim appeared first on NVIDIA Blog.
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Machine learning is a branch of artificial intelligence that allows computers to learn without being explicitly programmed. This article…
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The ability to identify non-trivial patterns in data using computational methods has sparked the creation of sophisticated machine intelligence systems with a wide range of crucial applications in science and technology. Such practices have primarily been used on general-purpose digital electronic processors (such as GPUs and CPUs), although this might result in undesirable computational latency and throughput restrictions.
Pavlovian associative learning is a fundamental type of learning that shapes both human and animal behavior. Ivan P. Pavlov demonstrated how dogs could learn to identify a ringing bell with food, leading a ring to result in salivation, in a famous experiment conducted more than a century ago. Pavlovian-style associative learning is no longer commonly used in artificial intelligence applications, despite the success of other learning theories such as backpropagation on artificial neural networks (ANNs). As stated in the papers, one reason behind this is that backpropagation method training on “traditional” ANNs requires a lot of processing and energy resources.
Continue reading | Checkout the paper
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The structure of a protein determines its functionality. Researchers have used this data in the past to design new drugs, vaccines, and enzymes. You can access the database for free here - https://www.deepmind.com/blog/alphafold-reveals-the-structure-of-the-protein-universe
This new database will allow researchers to gain a deeper understanding of protein families, how they interact and evolve, etc. Deepmind has written some use cases here - https://www.deepmind.com/blog/alphafold-reveals-the-structure-of-the-protein-universe
How would you use it? What would you like to explore or predict with it?
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Customer satisfaction is a potent metric that directly influences the profitability of an organization. With rapid technological advances in the past decade or so, it’s even more important to elevate customer focus in the following ways: Making your organization accessible to your customers across multiple modalities, including voice, text, social media, and more Providing your […]
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Automated defect detection using computer vision helps improve quality and lower the cost of inspection. Defect detection involves identifying the presence of a defect, classifying types of defects, and identifying where the defects are located. Many manufacturing processes require detection at a low latency, with limited compute resources, and with limited connectivity. Amazon Lookout for […]
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There have been many recent advancements in the NLP domain. Pre-trained models and fully managed NLP services have democratised access and adoption of NLP. Amazon Comprehend is a fully managed service that can perform NLP tasks like custom entity recognition, topic modelling, sentiment analysis and more to extract insights from data without the need of any prior […]
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Amazon SageMaker Studio is a web-based integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Each onboarded user in Studio has their own dedicated set of resources, such as compute instances, a home directory on an Amazon Elastic File System (Amazon EFS) volume, and […]
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This is the second part of a series that showcases the machine learning (ML) lifecycle with a data mesh design pattern for a large enterprise with multiple lines of business (LOBs) and a Center of Excellence (CoE) for analytics and ML. In part 1, we addressed the data steward persona and showcased a data mesh […]
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Organizations across various industries are using artificial intelligence (AI) and machine learning (ML) to solve business challenges specific to their industry. For example, in the financial services industry, you can use AI and ML to solve challenges around fraud detection, credit risk prediction, direct marketing, and many others. Large enterprises sometimes set up a center […]
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Hey all!
I'm Felix! I have a podcast and I interviewed Blake Lemoine earlier this week. The podcast is currently in post production and I wrote the teaser article (linked below) about it, and am happy to answer any Q's. I have a background in AI (phil) myself and really enjoyed the conversation, and would love to chat with the community here/answer Q's anybody may have. Thank you!
Teaser article here.
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Hello guys,
I tried to use Jax for the first time and I thought coding the DQN would be a good first test. I'm using the Haiku library and the general code structure from CleanRL.
My code: https://gist.github.com/nico-bohlinger/4c5b21464df0f3aaf555906b0959a4c5
Unfortunately the number of steps per second keeps steadily decreasing over time. Has somebody an idea why this is happening? If I use my variant of the CleanRL Pytorch version everything is fine. So I would guess something is wrong with the way I use Haiku / Jax.
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Artificial intelligence (AI) has been gaining popularity in the supply chain industry as it promises to help companies improve their…
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Researchers have added an additional step of pre training a generic image deep learning model on 800k chest x-ray images using supervised contrartsive learning using noisy labels from radiology reports. Image embeddings generated from this network can then be used for tasks like abnormality detection on a smaller set of chest x-ray images They have also released a chest foundation tool for generating image embeddings for chest x-ray. I liked the idea behind this paper and I believe it can also be extended to other medical imaging modalities like MR,CT. I have made a video on the same . Do checkout : https://youtu.be/lyhG6hivJqw
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Hey everyone!
I just published a blog post today that summarizes my undergraduate thesis work. The thesis topic is a multi-network approach to minimize overfitting to noisy data.
Here is a link to the article.
Any feedback or questions would be really appreciated.
Thanks!
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Having taken a couple of months to poke around with Replika.ai and checking other similar products like Kuki, I'm interested in crafting my own "robot companion" but I have no real knowledge of how to set up an AI. Are there any good options for someone who wants to make a bot, but doesn't really know the ins-and-outs of the design process? Open source would be my preference.
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This is a project to simply collect as many salary information in the whole AI/ML job space and make it all public for everyone to access and use (researchers, jobseekers, recruiters, etc.).
The dataset can be found here: https://salaries.ai-jobs.net/download/
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More than 160 years after the legendary Pony Express delivery service completed its first route, a new generation of “Pony”-emblazoned vehicles are taking an AI-powered approach to long-haul delivery. Autonomous driving company Pony.ai announced today a partnership with SANY Heavy Truck (SANY), China’s largest heavy equipment manufacturer, to jointly develop level 4 autonomous trucks. The Read article >
The post Pony.ai Express: New Autonomous Trucking Collaboration Powered by NVIDIA DRIVE Orin appeared first on NVIDIA Blog.
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Take a trip down memory lane this week with an instantly recognizable classic, Command & Conquer Remastered Collection, joining the nearly 20 Electronic Arts games streaming from the GeForce NOW library. Speaking of remastered, GeForce NOW members can enhance their gameplay further with improved resolution scaling in the 2.0.43 app update. When the feature is Read article >
The post Welcome Back, Commander: ‘Command & Conquer Remastered Collection’ Joins GeForce NOW appeared first on NVIDIA Blog.
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Selecting the right laptop is a lot like trying to pick the right major. Both can be challenging tasks where choosing wrongly costs countless hours. But pick the right one, and graduation is just around the corner. The tips below can help the next generation of artists select the ideal NVIDIA Studio laptop to maximize performance for the critical workload demands of their unique creative fields — all within budget.
The post NVIDIA Studio Laptops Offer Students AI, Creative Capabilities That Are Best in… Class appeared first on NVIDIA Blog.
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Sports produce a slew of data. In a game of cricket, for example, each play generates millions of video-frame data points for a sports analyst to scrutinize, according to Masoumeh Izadi, managing director of deep-tech startup TVConal. The Singapore-based company uses NVIDIA AI and computer vision to power its sports video analytics platform, which enables Read article >
The post How’s That? Startup Ups Game for Cricket, Football and More With Vision AI appeared first on NVIDIA Blog.
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Engineers working on “analog deep learning” have found a way to propel protons through solids at unprecedented speeds.
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Data representation using graphics such as charts, plots, infographics, heat maps, bubble clouds, scatter plots, mekko charts are referred to as data visualization. Such visual displays and representations of information help communicate complex data relationships and data-driven insights in a way that makes it easy to understand and base decisions on.
The post The Value of Real-Time Data Visualization and Interpretation appeared first on Data Science Central.
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In this video, I present a walkthrough of my poster "A Semi-automatic approach for Generating Video Trailers for Learning Pathways" that got accepted at the venue AIED 2022. I will be sharing the paper soon.
Let me know your thoughts 💭 Much Appreciated! 🤗
https://youtu.be/Y93GXvVERmk
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Excited to share that the latest Luminaire v0.4.0 release has several new capabilities with support up to python 3.10 and other package upgrades. Checkout the latest release here: https://github.com/zillow/luminaire
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https://preview.redd.it/uewvxpt7f4e91.png?width=1200&format=png&auto=webp&s=e2a3a89df1337b2df0f81d39cb75b4774e163bd7
outsystems-ai-reading-group.github.io for more info
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Pretty Jupyter is an easy-to-use package that creates beautifully styled and dynamic html webpage from Jupyter notebook. Its repo is available here: https://github.com/JanPalasek/pretty-jupyter .
Check out the demo and compare it with the default jupyter. You can try also Pretty Jupyter online without the need to install it.
Main Features
Visually appealing styles.
Automatic Table of Contents generation.
Tabsets: Tabs that hold section content inside them.
Using Python variables in Markdown: Helps in creating dynamic reports.
Code Folding: Show/Hide code to filter out unnecessary content.
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As enterprises move from running ad hoc machine learning (ML) models to using AI/ML to transform their business at scale, the adoption of ML Operations (MLOps) becomes inevitable. As shown in the following figure, the ML lifecycle begins with framing a business problem as an ML use case followed by a series of phases, including […]
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Here I explain my journey switching my career into Data Science in my late 30s, my thinking, motivations, expectations, courses I took…
Continue reading on Becoming Human: Artificial Intelligence Magazine »
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Anees is an Arabic chatbot that can speak to users on different topics or an open-domain multi-turn conversation rather than a specific domain. Anees is your personal AI friend that you can express and witness yourself through a helpful and empathetic conversation. Anees offers a set of features like natural language understanding, emotion classification, intent classification, weather/schedule, recommendation, and natural language generation.
For the code and implementation details: https://github.com/aashrafh/Anees
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Hello, I lost a bit of touch to the current SOTA of self-supervised pretraining of CNNs, in particular ResNet. I found this repository https://github.com/vturrisi/solo-learn that has many methods implemented but I'm not really sure where to start. My goal is to pretrain a ResNet backbone on a decently large amount of image data that comes from a certain domain and after that fine-tune it for different downstream tasks (classification, segmentation, object detection) on a subset of the data I have labels for.
Would be grateful for some tips how/where I should start and what the most promising SSL method would be.
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Quickly move your notebooks from research to production with no extra work!
https://www.youtube.com/watch?v=6Iyt9Wip3C4
Mage is an open-source code editor for transforming data and building ML pipelines.
Link to tool: https://github.com/mage-ai/mage-ai
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In the end, it came down to 213 thousandths of a second! That was the difference between the two best times in the finale of the first AWS AWS DeepRacer Student Wildcard event hosted in Ottawa, Canada this May. I watched in awe as 13 students competed in a live wildcard race for the AWS […]
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Logistics and transportation companies track ETA (estimated time of arrival), which is a key metric for their business. Their downstream supply chain activities are planned based on this metric. However, delays often occur, and the ETA might differ from the product’s or shipment’s actual time of arrival (ATA), for instance due to shipping distance or […]
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A century from now, historians will remark on a transformation that seemed subtle at the time but will have huge ramifications over time. Specifically, 2020 will be seen as the year when meetings became transparent.
The post DSC Weekly 26 July 2022: When Meetings Become Searchable appeared first on Data Science Central.
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Creativity heats up In the NVIDIA Studio as the July NVIDIA Studio Driver, available now, accelerates the recent Chaos V-Ray 6 for 3ds Max release.Plus, this week’s In the NVIDIA Studio 3D artist, Brian Lai, showcases his development process for Afternoon Coffee and Waffle, a piece that went from concept to completion faster with NVIDIA RTX acceleration in Chaos V-Ray rendering software.
The post July NVIDIA Studio Driver Improves Performance for Chaos V-Ray 6 for 3ds Max appeared first on NVIDIA Blog.
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The world of human resources is changing. New technologies are taking over the job of recruiting, managing employees, and even training…
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Hey! Found a curious recently published experiment with a tinyML magic wand on Hackster. Earlier, I saw the original experiment with TensorFlow Lite. It seems quite interesting to me that the author not only repeated but also surpassed the results of the original case. https://www.hackster.io/alexmiller11/making-famous-magic-wand-33x-faster-7ec19f
What are your thoughts?
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Hi r/MachineLearning,
The Inference Framework (TIF) is Cohere's platform for large Transformer language model inference. In this post, we share its high-level structure and some of the methods that help us serve massive language models more efficiently.
https://txt.cohere.ai/running-large-language-models-in-production-a-look-at-the-inference-framework-tif/
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AI has single-handedly improved communications, boosted productivity, facilitated seamless collaboration, and much more.
The post How is Artificial Intelligence Transforming Meeting Experiences appeared first on Data Science Central.
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What’s the value of my data? Today’s most critical question to which every organization should know the answer still goes unanswered in an age where the world’s most valuable resource is data.
The post What’s the Value of my Data? Today’s Most Critical Yet Hard to Answer Question appeared first on Data Science Central.
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Introduction In this post, I discussed the architecture of digital twins. This is a relatively new and emerging topic, and here I will look at Azure Digital Twins as an example to examine this architecture in depth. The architecture described below is from a cloud perspective It is created by integrating other existing cloud products I… Read More »An example of Digital Twins Architecture – Azure Digital Twins
The post An example of Digital Twins Architecture – Azure Digital Twins appeared first on Data Science Central.
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I've been building a memristor with the idea of creating a small hardware neural network, and am hoping someone has ideas for a neural net with only a handful of neurons (since I have to make each connection by hand). Ideas?
Thinking maybe something like a three input light tracker for a little solar panel, but was curious if there were other ideas.
https://bigattichouse.medium.com/penny-for-your-thoughts-copper-based-electrolytic-memristor-neural-network-part-4-8a6e43a3ce26
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“Researchers [from Wake Forest University, the University of California, and the University of Kentucky] performed surgery on 11 rats,” writes Michael Joseph Gross in “The Pentagon’s Push to Program Soldiers’ Brains” for The Atlantic:
Into each rat’s brain, an electronic array—featuring 16 stainless-steel wires—was implanted. After the rats recovered from surgery, they were separated into two groups, and they spent a period of weeks getting educated, though one group was educated more than the other. When the more educated group of rats attained mastery of this task, the researchers exported the neural-firing patterns recorded in the rats’ brains—the memory of how to perform the complex task—to a computer.
“What we did then was we took those signals and we gave it to an animal that was stupid,” Geoff Ling said at a DARPA event in 2015—meaning that researchers took the neural-firing patterns encoding the memory of how to perform the more complex task, recorded from the brains of the more educated rats, and transferred those patterns into the brains of the less educated rats—”and that stupid animal got it. They were able to execute that full thing.” Ling summarized: “For this rat, we reduced the learning period from eight weeks down to seconds.”
https://www.theatlantic.com/magazine/archive/2018/11/the-pentagon-wants-to-weaponize-the-brain-what-could-go-wrong/570841/
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This is a guest post co-written with Mutisya Ndunda from Trumid. Like many industries, the corporate bond market doesn’t lend itself to a one-size-fits-all approach. It’s vast, liquidity is fragmented, and institutional clients demand solutions tailored to their specific needs. Advances in AI and machine learning (ML) can be employed to improve the customer experience, […]
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As a professional digital sculptor, Marlon Nuñez is on a mission to make learning 3D art skills easier, smoother and more fun for all. And with the help of an NVIDIA RTX-powered Lenovo mobile workstation, he takes his 3D projects to the next level, wherever he goes. Nuñez is the art director and co-founder of Read article >
The post Digital Sculptor Does Heavy Lifting With Lightweight Mobile Workstation appeared first on NVIDIA Blog.
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Artificial Intelligence(AI) has taken the world of tech by a blast. AI in the FinTech market is being utilized at a rising rate. It is…
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Paper: https://arxiv.org/abs/2207.10642 Code: https://github.com/apple/ml-gmpi Webpage: https://xiaoming-zhao.github.io/projects/gmpi/
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https://youtu.be/1gHUiNLYa20
This video explains and summarizes the 57 pages long "Building Machine Translation Systems for the Next Thousand Languages." paper from Google Research. It goes into the data collection, modelling processes and a bit into the results.
Paper link: https://arxiv.org/abs/2205.03983
Outline:
00:00 Machine translation for a 1000 languages
00:42 Weights&Biases (Sponsor)
02:00 Problems with many languages
04:15 Collecting data for 1k languages
11:46 Building MT models
14:13 Results on a thousand languages
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Data mining : Linkedin Profile Scraper integrated with Language recognition to assign profile grades - YouTube
Based on the keyword provided the software will search for profiles and assign scores, It does around 50profiles per minut so It can check automatically 3000 profiles an hour and assign a score to each profile based on the keywords loaded.
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Decision Tree Algorithm, Support Vector Method Algorithm, Logistic Regression, K-means Clustering Algorithm, and Naïve Bayesian…
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AI Weirdness: the strange side of machine learning
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“Interpretability methods” seek to shed light on how machine-learning models make predictions, but researchers say to proceed with caution.
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PROOF: https://i.redd.it/2z42nlnbssc91.jpg
We’re part of the team behind Meta AI’s latest AI breakthrough in machine translation with our No Language Left Behind (NLLB) project. It’s a translation system that can support over 200 languages, even if there isn't a lot of text available to learn from. The reality is that a handful of languages dominate the web meaning only a fraction of the world can access content and contribute to the web in their own language. We want to change this by creating more inclusive machine translations systems – ones that unlock access to the web for the more than 4B people around the world that are currently excluded because they do not speak one of the few languages content is available in. Here are a few things about NLLB we’re excited for:
Latest breakth…
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Domain knowledge can sometimes boost model performance significantly. I have used knowledge graph in some of my projects as pre/post step to improve model performance. But that adds to deployment complexity. Theseus is good step towards end to end AI models that incorporate domain knowledge. It is library for differentiable nonlinear least squares (NLS) that is particularly useful for applications like robotics and computer visions.
Read more: https://ai.facebook.com/blog/theseus-a-library-for-encoding-domain-knowledge-in-end-to-end-ai-models/
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Artificial Intelligence for Business Leaders Webinar
Join Professor Pedram Mokrian to learn how business leaders should think about developing AI solutions. Learn key AI terms, trends, and concepts that inform business strategy. Register for webinar.
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The process of building a machine learning (ML) model is iterative until you find the candidate model that is performing well and is ready to be deployed. As data scientists iterate through that process, they need a reliable method to easily track experiments to understand how each model version was built and how it performed. […]
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South Korean startup Lunit, developer of two FDA-cleared AI models for healthcare, went public this week on the country’s Kosdaq stock market. The move marks the maturity of the Seoul-based company — which was founded in 2013 and has for years been part of the NVIDIA Inception program that nurtures cutting-edge startups. Lunit’s AI software Read article >
The post Shifting Into High Gear: Lunit, Maker of FDA-Cleared AI for Cancer Analysis, Goes Public in Seoul appeared first on NVIDIA Blog.
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Epic Games is bringing a new Fortnite reward to GeForce NOW, available to all members. Drop from the Battle Bus in Fortnite on GeForce NOW between today and Thursday, Aug. 4, to earn “The Dish-stroyer Pickaxe” in game for free. Members can earn this item by streaming Fortnite on GeForce NOW Read article >
The post Get Battle Ready With New GeForce NOW Fortnite Reward appeared first on NVIDIA Blog.
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Thanks to earbuds you can have calls anywhere while doing anything. The problem: those on the other end of the call hear it all, too, from your roommate’s vacuum cleaner to background conversations at the cafe you’re working from. Now, work by a trio of graduate students at the University of Washington who spent the Read article >
The post Researchers Use GPUs to Give Earbud Users a ‘Mute Button’ for Background Noise appeared first on NVIDIA Blog.
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Business approach is changing constantly to all creative possibilities provided by the digital revolution. The big bang of the digital…
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I was googling around as I'm starting to get interested in AI and these videos came up in the search, I clicked on it despite thinking it was going to be low value but am now intrigued as to what they are for!
Any Ideas?
https://www.youtube.com/watch?v=RfNtuHQ42v8
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OpenAI blog post.
How DALL·E Credits Work.
Links to DALL-E Content policy and Terms of use, along with older archived versions.
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🍰 Slice of ML 🍰
Hi folks,
We recently built out a nice CLI that allows you to fetch the top ML tweets of the day/week! You can see a demo in the video above.
You can check out how we built it here & check out the repo here!
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Geometric Deep Learning approaches a broad class of ML problems from the perspectives of symmetry and invariance, providing a common blueprint for neural network architectures as diverse as CNNs, GNNs, and Transformers.
In a new series of posts, we study how these ideas have taken us from ancient Greece to convolutional neural networks.
Blog post link.
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As new data privacy regulations like GDPR (General Data Protection Regulation, 2017) have come into effect, customers are under increased pressure to monetize media assets while abiding by the new rules. Monetizing media while respecting privacy regulations requires the ability to automatically extract granular metadata from assets like text, images, video, and audio files at […]
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Large-scale models are revolutionizing deep learning and AI research, driving major improvements in language understanding, generating creative texts, multi-lingual translation and many more. But despite their remarkable capabilities, the models’ large size creates latency and cost constraints that hinder the deployment of applications on top of them. In particular, increased inference time and memory consumption […]
The post DeepSpeed Compression: A composable library for extreme compression and zero-cost quantization appeared first on Microsoft Research.
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Gaming has moved from a niche sector to the mainstream. Games have become a part of everyday lexicon like never before, and the technological progress evident within game UIs has played a role. The gaming landscape is highly diverse.
The post 4 Ways AI is Shaping the Future of Interactive Games appeared first on Data Science Central.
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Introduction Portable radio communication devices like walkie-talkie radios have supported security services for ages. Radio communication first gained traction during World War 1 when the military used Walkie-Talkie Radios exclusively to stay connected with their troops. Cut to today, we see security agents who are in charge of protecting people or property, using walkie-talkie radios… Read More »6 Reasons Why Today’s Physical Security Teams Can’t Rely on Walkie-Talkie Radios
The post 6 Reasons Why Today’s Physical Security Teams Can’t Rely on Walkie-Talkie Radios appeared first on Data Science Central.
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Announcements Achieving endpoint visibility to ward off the threat of a breach has never been more important than it is in the age of data proliferation and hybrid workplaces. Multiple endpoints and locations heighten that risk, making it essential for CISOs and IT security teams to overcome common challenges. Find out how organizations can reach… Read More »DSC Weekly 19 July 2022: From Knowledge Graphs to Transformation as a Service
The post DSC Weekly 19 July 2022: From Knowledge Graphs to Transformation as a Service appeared first on Data Science Central.
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AI and electric vehicle technology breakthroughs are transforming the automotive industry. These developments pave the way for new innovators, attracting technical prowess and design philosophies from Silicon Valley. Mike Bell, senior vice president of digital at Lucid Motors, sees continuous innovation coupled with over-the-air updates as key to designing sustainable, award-winning intelligent vehicles that provide Read article >
The post Lucid Motors’ Mike Bell on Software-Defined Innovation for the Luxury EV Brand appeared first on NVIDIA Blog.
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Methods that make a machine-learning model’s predictions more accurate overall can reduce accuracy for underrepresented subgroups. A new approach can help.
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When we work on a machine learning problem related to images, not only we need to collect some images as training data, but also need to employ augmentation to create variations in the image. It is especially true for more complex object recognition problems. There are many ways for image augmentation. You may use some […]
The post Image Augmentation with Keras Preprocessing Layers and tf.image appeared first on Machine Learning Mastery.
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Hey guys! Let me share with you a cool piece that I came across in the latest IEEE newsletter issue. It’s a guide that covers a new approach to creating tinyML models. Hope you’ll find it useful: https://iot.ieee.org/newsletter/july-2022/automated-design-of-tiny-machine-learning-models-a-practical-guide-part-1
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Are you struggling to get clicks on your Google Ads? You’re not alone. In fact, most people don’t know how to write headlines that get clicked. If you’re ready to learn how to write headlines that get more clicks, then this blog post is for you. You’ll learn some great headline writing tips that will… Read More »Google Ads Headlines: How To Write Headlines That Get More Clicks
The post Google Ads Headlines: How To Write Headlines That Get More Clicks appeared first on Data Science Central.
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Over the last few years, online education platforms have seen an increase in adoption of and an uptick in demand for video-based learnings because it offers an effective medium to engage learners. To expand to international markets and address a culturally and linguistically diverse population, businesses are also looking at diversifying their learning offerings by […]
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Renewable resources like sunlight provide a sustainable and carbon neutral mechanism to generate power. Governments in many countries are providing incentives and subsidies to households to install solar panels as part of small-scale renewable energy schemes. This has created a huge demand for solar panels. Reaching out to potential customers at the right time, through […]
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Intel has recently released Neural Compressor, an open-source Python package for model compression. This library can be applied to deep learning deployment on CPUs or GPUs to decrease the model size and speed up inference. Additionally, it offers a uniform user interface for well-known network compression techniques, including quantization, pruning, and knowledge distillation across various deep learning frameworks. The tool’s automatic accuracy-driven tweaking technique can be utilized to generate the best-quantized model. Additionally, it allows knowledge distillation so that the knowledge from the teacher model may be transferred to the student model. It implements several weight pruning methods to produce pruned models using a predetermined sparsity goal. For improved framework interoperability, the Python library also offers APIs for various deep learning frameworks, including TensorFlow, PyTorch, and MXNet.
Continue reading | The Github repo for the library can be accessed here.
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📚 Release notes:
👉 https://github.com/kornia/kornia/releases/tag/v0.6.6
📚 Docs and tutorials
👉 https://kornia.readthedocs.io/en/latest/
https://preview.redd.it/fu46z17xtac91.png?width=1060&format=png&auto=webp&s=e10c42173fca97e76d9e2ccdea8809f112c4392b
https://preview.redd.it/xy64c27xtac91.png?width=640&format=png&auto=webp&s=789a197ab894ac0f5716e276aff360b09fdfb8eb
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The Golden State Warriors won the 2022 National Basketball Association (NBA) title for many reasons. Having one of the top 10 players in NBA history in Steph Curry certainly helps. But other teams have top 10 / top 15 players, and they didn’t make the finals (or even get into the playoffs, in one case).… Read More »What Do NBA Champions and CDOs have in Common? Success Requires Being 2-way Players
The post What Do NBA Champions and CDOs have in Common? Success Requires Being 2-way Players appeared first on Data Science Central.
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Amazon Polly, an AI generated text-to-speech service, enables you to automate and scale your interactive voice solutions, helping to improve productivity and reduce costs. As our customers continue to use Amazon Polly for its rich set of features and ease of use, we have observed a demand for the ability to simultaneously generate synchronized audio […]
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Amazon Rekognition allows you to mitigate fraudulent attacks and minimize onboarding friction for legitimate customers through a streamlined identity verification process. This can result in an increase in customer trust and safety. Key capabilities of this solution include: Register a new user using a selfie Register a new user after face match against an ID […]
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Today, we are implementing a new technique so that DALL·E generates images of people that more accurately reflect the diversity of the world’s population. This technique is applied at the system level when DALL·E is given a prompt describing a person that does not
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NVIDIA Fleet Command — a cloud service for deploying, managing and scaling AI applications at the edge — now includes features that enhance the seamless management of edge AI deployments around the world. With the scale of edge AI deployments, organizations can have up to thousands of independent edge locations that must be managed by Read article >
The post Living on the Edge: New Features for NVIDIA Fleet Command Deliver All-in-One Edge AI Management, Maintenance for Enterprises appeared first on NVIDIA Blog.
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Technology company CORSAIR and streaming partner BigCheeseKIT step In the NVIDIA Studio this week. A leader in high-performance gear and systems for gamers, content creators and PC enthusiasts, CORSAIR has integrated NVIDIA Broadcast technologies into its hardware and iCUE software. Similar AI enhancements have also been added to Elgato’s audio and video software, Wave Link and Camera Hub.
The post CORSAIR Integrates NVIDIA Broadcast’s Audio, Video AI Features in iCUE and Elgato Software This Week ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
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For many organizations, trusting their data to the cloud requires having a complete understanding of and control over the environment in which that data resides and how it’s being processed. Microsoft understands this, and we are committed to building a trustworthy cloud—one in which security, privacy, and transparency are built into its core. A key […]
The post Confidential Containers: Verifiably secure computation in the cloud appeared first on Microsoft Research.
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As the name suggests, synthetic data is the data that is artificially generated rather than being created by actual events. In marketing…
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1) I asked OpenAI what kind of web application I should make to help make data analysts more efficient.
It responded by telling me to build an app using NLP to provide people with Excel formulas based on a given prompt.
2) I told OpenAI the idea in a separate API request and asked it for an available domain name.
It gave me www.excelformulabot.com, which I built.
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I have a dataset of tagged and linked object bounding-boxes in sequential video frames. If that isn't clear, you can watch a demo here:
https://www.youtube.com/watch?v=QKxSzFaHsbc
For various reasons, it's possible that a trajectory could be 'broken' in the dataset. Quick visual scanning doesn't allow detection of a break in a single trajectory; there are so many horizontal links, it's tough to notice one of them being missing.
How would you economically eliminate a small percentage of breaks in trajectories?
Some things I've thought of:
* Bootstrapping, i.e. using a trained network to predict -> this is a bit complex, it's possible but not my first choice
* Build a tool to view all linked detections overlaid in a single frame (doesn't immediately identify broken trajectories, but it might help)
Is there any simple UI I can build to easily identify broken trajectories in the dataset?
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The Python package {copent} v0.3 now available on PyPI, with the new function 'mvnt' that implements the method for estimating the copula entropy-based statistic for multivariate normality test. See arXiv:2206.05956 for more details.
GITHUB: https://github.com/majianthu/pycopent
PyPI: https://pypi.org/project/copent/
Your comments are welcome.
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Here is the list of all >1,200 ICML 2022 (International Conference on Machine Learning) papers, and a highlight for each of them. ICML 2022 will take place from July 17 at Baltimore.
https://www.paperdigest.org/2022/07/icml-2022-highlights/
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UC Berkeley and Google AI Researchers Introduce ‘Director’: a Reinforcement Learning Agent that Learns Hierarchical Behaviors from Pixels by Planning in the Latent Space of a Learned World Model. The world model Director builds from pixels allows effective planning in a latent space. To anticipate future model states given future actions, the world model first maps pictures to model states. Director optimizes two policies based on the model states’ anticipated trajectories: Every predetermined number of steps, the management selects a new objective, and the employee learns to accomplish the goals using simple activities. The direction would have a difficult control challenge if they had to choose plans directly in the high-dimensional continuous representation space of the world model. To reduce the size of the discrete codes created by the model states, they instead learn a goal autoencoder. The goal autoencoder then transforms the discrete codes into model states and passes them as goals to the worker after the manager has chosen them.
✅ Director agent learns practical, general, and interpretable hierarchical behaviors from raw pixels
✅ Director successfully learns in a wide range of traditional RL environments, including Atari, Control Suite, DMLab, and Crafter
✅ Director outperforms exploration methods on tasks with sparse rewards, including 3D maze traversal with a quadruped robot from an egocentric camera and proprioception
Continue reading| Checkout the paper and project
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I would like to share personal insights about doing great research and towards being a globally leading researcher:
Not all our research legacies are correct or will be corrected shortly, so just keep taking the initiative to correct them. https://openreview.net/forum?id=xENf4QUL4LW¬eId=C2eCHs2k6CM.
Not all our papers get cited or published, so when our papers serve as a great foundation for other works, just keep positive and confident to deliver them to more people who may be interested.
Reddit discussion
Linkedin discussion
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CUHK released aDeepFashion-MultiModal dataset with rich multi-modal annotations, including manually annotated human parsing labels, manually annotated human keypoints, manually annotated fine-grained labels and textual descriptions in June 2022. Since then, researchers have been looking to work with the dataset, fine-tune it with CLIP model and different metrics.
While finetuning I understand is an imp. process and a difficult one, they claim to have gained 217% Delta increase on Recall metric. When I have been trying to run it, my laptop has not been so capable to run this, so I am looking for alternative for remote GPU.
But, is this growth of 217% from pertained to fine-tuned model even possible? A bit hard to believe. If so, is Colab a good option to run remote GPU while being able to make use of the functionality?
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A detailed and insightful study by MetaAI team on the memorization, overfitting and forgetting in LLMs.
The paper talks about how different definitions of "memorization" and how scaling affects the amount of training data that the large language models can memorize during the training phase. Studies are also presented on how the forgetting curves look like and how overfitting relates to memorization for these large language models. The Appendix section is a gold mine as well.
Annotated version of the paper - Github Link
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A detailed and insightful study by MetaAI team on the memorization, overfitting and forgetting in LLMs.
The paper talks about how different definitions of "memorization" and how scaling affects the amount of training data that the large language models can memorize during the training phase. Studies are also presented on how the forgetting curves look like and how overfitting relates to memorization for these large language models. The Appendix section is a gold mine as well.
Annotated version of the paper - Github Link
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Hi folks,
I was working on a personal experimental project, which I thought of making it open source now. It saves much time for literature research.
If you are an industrial researcher or in academia, you probably spend much time reading research articles and news related to your topic.
If you try to search papers related to your topic, finding relevant documents on the internet takes time. You probably know the pain of extracting citations of articles from different websites.
Previously I used to fetch papers from google or semantic scholar, but semantic scholar does not show correct paper citations.
I am excited to announce RESP: Research Papers Search
Features:
Fetch all citations of a single paper from Google Scholar in CSV format
Fetch all related papers of a single paper from Google Scholar in CSV format
Fetch all connected papers from connectedpapers.com (it does not use a citation tree, it uses similarity to build graphs) in CSV format
Fetch relevant papers based on keywords from different sources, including Arxiv, ACL, ACM, PMLR, NeurIPS, cvf etc., in CSV format
GITHUB: https://github.com/monk1337/resp
Examples: https://github.com/monk1337/resp/tree/main/examples
I hope it will be helpful in your research. Thanks :)
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Today, social media is a huge source of news. Users rely on platforms like Facebook and Twitter to consume news. For certain industries such as insurance companies, first respondents, law enforcement, and government agencies, being able to quickly process news about relevant events occurring can help them take action while these events are still unfolding. […]
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Australian animator Marko Matosevic is taking jokes from a children’s school dads’ group and breathing them into animated life with NVIDIA Omniverse, a virtual world simulation and collaboration platform for 3D workflows.
The post Meet the Omnivore: Animator Entertains and Explains With NVIDIA Omniverse appeared first on NVIDIA Blog.
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AI Weirdness: the strange side of machine learning
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Image dump 1
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This is a guest blog post by Danny Brock, Rajeev Govindan and Krishnaram Kenthapadi at Fiddler AI. Your Amazon SageMaker models are live. They’re handling millions of inferences each day and driving better business outcomes for your company. They’re performing exactly as well as the day they were launched. Er, wait. Are they? Maybe. Maybe […]
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Data scientists often work towards understanding the effects of various data preprocessing and feature engineering strategies in combination with different model architectures and hyperparameters. Doing so requires you to cover large parameter spaces iteratively, and it can be overwhelming to keep track of previously run configurations and results while keeping experiments reproducible. This post walks […]
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Organizations are increasingly building and using machine learning (ML)-powered solutions for a variety of use cases and problems, including predictive maintenance of machine parts, product recommendations based on customer preferences, credit profiling, content moderation, fraud detection, and more. In many of these scenarios, the effectiveness and benefits derived from these ML-powered solutions can be further […]
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Sponsored Post If you’re a data engineer or data scientist, you know how hard it is to generate and maintain realistic data at scale. And to guarantee data privacy protection, in addition to all your day-to-day responsibilities? OOF. Talk about a heavy lift. But in today’s world, efficient data de-identification is no longer optional for […]
The post High-Fidelity Synthetic Data for Data Engineers and Data Scientists Alike appeared first on Machine Learning Mastery.
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Check out our new open source code editor for transforming data and building ML pipelines: https://github.com/mage-ai/mage-ai
If you’re available, I’d love to hop on a quick Zoom to help you get set up.
In the meantime, here is the install guide: https://github.com/mage-ai/mage-ai#using-pip and a short tutorial: https://github.com/mage-ai/mage-ai/blob/master/docs/tutorials/train_titanic_model/README.md
I’d love to get your feedback on whether this is useful to you or not. Thank you so much!
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As part of our DALL·E 2 research preview, more than 3,000 artists from more than 118 countries have incorporated DALL·E into their creative workflows. The artists in our early access group have helped us discover new uses for DALL·E and have served as
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Precision agriculture has recently shown a lot of interest in computer vision technology. Computer vision, at the heart of robotics and…
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Investigate the ultimate truth this GFN Thursday with Loopmancer, now streaming to all members on GeForce NOW. Stuck in a death loop, RTX 3080 and Priority members can search for the truth with RTX ON — including NVIDIA DLSS and ray-traced reflections. Plus, players can enjoy the latest Genshin Impact event with the “Summer Fantasia” Read article >
The post Action on Repeat: GFN Thursday Brings Loopmancer With RTX ON to the Cloud appeared first on NVIDIA Blog.
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Announcements Achieving endpoint visibility to ward off the threat of a breach has never been more important than it is in the age of data proliferation and hybrid workplaces. Multiple endpoints and locations heighten that risk, making it essential for CISOs and IT security teams to overcome common challenges. Find out how organizations can reach… Read More »DSC Weekly 12 July 2022: The Emergence of the Modern Studio Model
The post DSC Weekly 12 July 2022: The Emergence of the Modern Studio Model appeared first on Data Science Central.
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Twitter thread:
https://twitter.com/karpathy/status/1547332300186066944
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It looks like chronic kidney disease diagnosis has been solved in this paper: https://ieeexplore.ieee.org/document/8693581
I mean no disrespect to the authors, but this publication makes me slightly doubt the peer-review system. Or I am just such an amateur, that I am not seeing the brilliance behind this paper, which is also possible.
Have a read through it yourselves
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SimSwap (https://github.com/neuralchen/SimSwap) is basically a framework that carries out face-swapping in a similar way deepfake technology does with a source and a target video. However, for the source, only one image is required. Not sure how this would work since 1 image isn't enough for actual training. Is this simply face mapping? I feel like the output is a bit too sophisticated for that.
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In this seminar Aditya introduces a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. Watch on YouTube.
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Artificial intelligence (AI) has become synonymous with assistance and efficiency. From a technology that was looked at with mistrust as…
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https://preview.redd.it/xjtcha3r35b91.png?width=1298&format=png&auto=webp&s=00873223c1ea0c6afcd5e22c7645521036b7e341
This post presents a way to run transformers models via the Python C API. The referenced notebook loads two txtai workflows, one that translates English to French and another that summarizes a webpage. After loading the models through C code, another example runs the workflows through assembly to show this works with any native code.
Full code links: Notebook | GitHub
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BigScience Project introduces BLOOM (BigScience Large Open-science Open-access Multilingual Language Model), the first multilingual Large Language Model (LLM) trained in complete transparency by the largest group of AI academics. Unlike the traditional secrecy of industrial AI research laboratories, the project demonstrates the possibility of training promising AI models published by the larger research community responsibly and openly.
✅ Transformers-based LLM
✅ 176B parameters (larger than GPT-3 and OPT-175B)
✅ Trained on 1.6TB text data, the equivalent of 320 times the complete works of Shakespeare
Continue reading | Download
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Touring vehicles just became a little more grand. Electric vehicle maker Human Horizons provided a detailed glimpse earlier this month of its latest production model, the GT HiPhi Z. The intelligent EV is poised to redefine the grand tourer category with innovative, software-defined capabilities that bring luxurious cruising to the next level. The vehicle’s marquee Read article >
The post Grand Entrance: Human Horizons Unveils Smart GT Built on NVIDIA DRIVE Orin appeared first on NVIDIA Blog.
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Kristel Michielsen was into quantum computing before quantum computing was cool. The computational physicist simulated quantum computers as part of her Ph.D. work in the Netherlands in the early 1990s. Today, she manages one of Europe’s largest facilities for quantum computing, the Jülich Unified Infrastructure for Quantum Computing (JUNIQ) . Her mission is to help Read article >
The post Merge Ahead: Researcher Takes Software Bridge to Quantum Computing appeared first on NVIDIA Blog.
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Visual effects savant Surfaced Studio steps In the NVIDIA Studio this week to share his clever film sequences, Fluid Simulation and Destruction, as well as his creative workflows. These sequences feature quirky visual effects that Surfaced Studio is renowned for demonstrating on his YouTube channel.
The post Sequences That Stun: Visual Effects Artist Surfaced Studio Arrives ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
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A geometric deep-learning model is faster and more accurate than state-of-the-art computational models, reducing the chances and costs of drug trial failures.
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Web analytics tools offer vital insights into your website’s visitors’ behavior by tracking their real-time activities on the platform from behind. These tools study almost everything – the number of daily and regular visitors, sessions and duration, conversions, and beyond. You can access a comprehensive report covering every aspect and personalize it to focus on… Read More »Web Analytics Dashboards Carry a World of Data for Various Purposes
The post Web Analytics Dashboards Carry a World of Data for Various Purposes appeared first on Data Science Central.
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Convolutional neural networks have been found successful in computer vision applications. Various network architectures are proposed and they are neither magical nor hard to understand. In this tutorial, we will make sense of the operation of convolutional layers and their role in a larger convolutional neural network. After finishing this tutorial, you will learn: How […]
The post Understanding the Design of a Convolutional Neural Network appeared first on Machine Learning Mastery.
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Scrapy is highly customizable and developer friendly crawling framework in Python. It can help you build in few line wonderful crawler to…
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A re-implementation of the famous 2020 paper - "Extracting Training Data from Large Language Models" by Nicholas Carlini, Florian Tramer et al.
Code - https://github.com/shreyansh26/Extracting-Training-Data-from-Large-Langauge-Models
The official implementation is great and I definitely learned a few things from it. In the re-implementation, I have also included the temperature-decay sampling and sliding-window-based minimum perplexity metric which was not present in the official implementation.
I checked the extracted Samples (refer to the Github repo) and they surely contained some memorized information.
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An awesome collection of Federated learning & Blockchain research papers in the Healthcare domain.
Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy preservation. This repo contains a curated list of Federated Learning papers/resources and recent advancements in Healthcare.
As of now ~330 papers
Pr's welcome
https://github.com/monk1337/Aweome-Heathcare-Federated-Learning
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The unveiling by U.S. President Joe Biden Monday of the first full-color image from the James Webb Space Telescope is already astounding — and delighting — humans around the globe. “We can see possibilities nobody has ever seen before, we can go places nobody has ever gone before,” Biden said during a White House press Read article >
The post AI on the Sky: Stunning New Images From the James Webb Space Telescope To Be Analyzed by, Train, AI appeared first on NVIDIA Blog.
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Engineers are using the NVIDIA Omniverse 3D simulation platform as part of a proof of concept that promises to become a model for putting green energy to work around the world. Dubbed Gigastack, the pilot project — led by a consortium that includes Phillips 66 and Denmark-based renewable energy company Ørsted — will create low-emission Read article >
The post Windfall: Omniverse Accelerates Turning Wind Power Into Clean Hydrogen Fuel appeared first on NVIDIA Blog.
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LiDAR is a key enabling technology in growing autonomous markets, such as robotics, industrial, infrastructure, and automotive. LiDAR delivers precise 3D data about its environment in real time to provide “vision” for autonomous solutions. For autonomous vehicles (AVs), nearly every carmaker uses LiDAR to augment camera and radar systems for a comprehensive perception stack capable […]
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We live in a data-rich world. Very data rich. Indeed, it’s estimated that roughly 2.5 quintillion bytes of data are created every day. Perhaps because of its ubiquity, there are those who believe the sheer volume of available data means we have all we need to easily and accurately answer any question without delay. If… Read More »Why We Need to Move From Data-First to a Knowledge-First World
The post Why We Need to Move From Data-First to a Knowledge-First World appeared first on Data Science Central.
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The tech industry is abuzz with hyped up pontifications and bold predictions of the business-changing potential of Data Products. I could not be happier as it’s a topic I have explored in several blogs (see the end of this blog for a list of my blogs on Data Products…yea, I know, get a life). A… Read More »Critical Role of Analytic Profiles in Developing Data Products
The post Critical Role of Analytic Profiles in Developing Data Products appeared first on Data Science Central.
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According to the McKinsey Report called Value Creation in the Metaverse: $120b+ in investment has flowed into the metaverse so far in 2022 79% of consumers active on the metaverse have made a purchase >15% of corporate revenue is expected to come from the metaverse in the next 5 years according to 25% of senior… Read More »Metaverse use cases – Which industries could the metaverse impact?
The post Metaverse use cases – Which industries could the metaverse impact? appeared first on Data Science Central.
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Executing Industrial Internet of Things (IIoT) solutions is vital as the most competitive global manufacturing companies are becoming digital enterprises. Industrial Internet of Things (IIoT) solutions and platforms are leading the reshaping and transformation of landscapes. A pre-built Industrial Internet of Things (IIoT) solution offers the benefit of a ready-made “IoT development kit” with the… Read More »Features of IIoT (Industrial Internet of Things) Seamless Connectivity and Data Acquisition
The post Features of IIoT (Industrial Internet of Things) Seamless Connectivity and Data Acquisition appeared first on Data Science Central.
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On Wednesday, July 13th at 11 am EST, please join DQLabs for an exclusive virtual event“Defining Data Relevance: The rise of the Modern Data Stack and the Modern Data Quality Platform”. The data producers, consumers, and leaders deserve an ecosystem that delivers the data that is relevant to them – one size fits all approaches… Read More »Webinar Series -The rise of the Modern DataStack and the Modern Data Quality Platform
The post Webinar Series -The rise of the Modern DataStack and the Modern Data Quality Platform appeared first on Data Science Central.
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IT leaders are running into several RPA failures. Here, we have covered the top 7 reasons why RPA implementations fail and how you can…
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Crawling a website is as today an essential skill for anyone working in or with the digital industry. Firstly, I will start by clarifying…
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Google Imagen: a machine learning system that can generate graphics from text input.
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https://reddit.com/link/vw3tkf/video/xe0t4pumpta91/player
https://reddit.com/link/vw3tkf/video/7pf9dl3npta91/player
Programming
Function from Description
Code to Explanation
Fix invalid Code
Translate Languages
Class from Description
Get Language from Code
Function from Docstring
Helpers
Regex from Description
Regex to Explanation
Linux Command
Get time complexity
Git Command from Description
Database
Text Description to SQL Command
Web
Generate HTML from Description
CSS from Description
Meta Tags from Description
I think this could be helpful to a lot of people (especially for beginner programmers). You can check out all functionalities on your own here:
programming-helper.com
Have fun using the tool ❤️
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Hello, the title says it all. I'm trying to find any ressources (mainly aligned corpus) that could be helpful in identifying and simplifying complex sentences in French. ALECTOR is the only one I stumbled upon.
Do you have any resources or tips? I was wondering if searching for book and their simplified version could be useful but I fear it would be more like learning to translate old french into modern french.
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